Stations

General overview

# install.packages("remotes")
# remotes::install_github("kwb-r/wasserportal", upgrade = "never", force = TRUE)
library(wasserportal)
overview_options <- wasserportal::get_overview_options()
str(overview_options)
#> List of 2
#>  $ surface_water:List of 7
#>   ..$ water_level         : chr "ws"
#>   ..$ flow                : chr "df"
#>   ..$ level               : chr "wt"
#>   ..$ conductivity        : chr "lf"
#>   ..$ ph                  : chr "ph"
#>   ..$ oxygen_concentration: chr "og"
#>   ..$ oxygen_saturation   : chr "os"
#>  $ groundwater  :List of 2
#>   ..$ level  : chr "gws"
#>   ..$ quality: chr "gwq"

system.time(stations <- wasserportal::get_stations())
#> Importing 9 station overviews from Wasserportal Berlin ... ok. (8.71s)
#>    user  system elapsed 
#>   0.055   0.010   9.000

str(stations)
#> List of 3
#>  $ overview_list:List of 9
#>   ..$ surface_water.water_level         : tibble [72 × 9] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: int [1:72] 5865900 5827103 5865300 5819900 5864801 5861101 5800107 5800317 5867003 5867401 ...
#>   .. ..$ Messstellenname  : chr [1:72] "Allee der Kosmonauten" "Allendestraße" "Am Bahndamm" "Am Freibad" ...
#>   .. ..$ Gewaesser        : chr [1:72] "M.-H.-Grenzgr." "Müggelspree" "Wuhle" "Tegeler Fließ" ...
#>   .. ..$ Betreiber        : chr [1:72] "SenUVK" "SenUVK" "SenUVK" "SenUVK" ...
#>   .. ..$ Datum            : chr [1:72] "15.03.2022 05:45" "15.03.2022 05:45" "15.03.2022 00:30" "15.03.2022 05:45" ...
#>   .. ..$ Wasserstand      : int [1:72] 5 50 93 92 4 25 65 76 69 37 ...
#>   .. ..$ Einheit          : chr [1:72] "cm" "cm" "cm" "cm" ...
#>   .. ..$ Ganglinien       : logi [1:72] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:72] "niedrig" "niedrig" "niedrig" "normal" ...
#>   ..$ surface_water.flow                : tibble [16 × 9] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: int [1:16] 5827103 5865300 5864801 5867401 5867900 5827101 5870100 5826701 5862811 5827700 ...
#>   .. ..$ Messstellenname  : chr [1:16] "Allendestraße" "Am Bahndamm" "Am Kienberg" "Bürgerpark" ...
#>   .. ..$ Gewaesser        : chr [1:16] "Müggelspree" "Wuhle" "Hellersdorfer Graben" "Panke" ...
#>   .. ..$ Betreiber        : chr [1:16] "SenUVK" "SenUVK" "SenUVK" "SenUVK" ...
#>   .. ..$ Datum            : chr [1:16] "15.03.2022 05:45" "15.03.2022 00:30" "14.03.2022 10:00" "15.03.2022 02:15" ...
#>   .. ..$ Durchfluss       : num [1:16] 3.6 0.187 0.001 1.03 0.96 4.22 9.91 7 9.7 19.6 ...
#>   .. ..$ Einheit          : chr [1:16] "m³/s" "m³/s" "m³/s" "m³/s" ...
#>   .. ..$ Ganglinie        : logi [1:16] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:16] "keine" "niedrig" "keine" "normal" ...
#>   ..$ surface_water.level               : tibble [61 × 9] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: chr [1:61] "601" "151" "153" "509" ...
#>   .. ..$ Messstellenname  : chr [1:61] "MPS Berlin-Spandauer-Schifffahrtskanal" "MPS Caprivibrücke" "MPS Charlottenburg" "MPS Landwehrkanal" ...
#>   .. ..$ Gewaesser        : chr [1:61] "Berlin-Spandauer-Schifffahrtskanal" "Spree" "Spree" "Landwehrkanal" ...
#>   .. ..$ Betreiber        : chr [1:61] "Land Berlin" "Land Berlin" "Land Berlin" "Land Berlin" ...
#>   .. ..$ Datum            : chr [1:61] "26.10.2021 10:15" "26.10.2021 10:45" "26.10.2021 11:00" "26.10.2021 12:15" ...
#>   .. ..$ Wassertemperatur : chr [1:61] "10.44" "10.13" "10.22" "9.96" ...
#>   .. ..$ Einheit          : chr [1:61] "°C" "°C" "°C" "°C" ...
#>   .. ..$ Ganglinie        : logi [1:61] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:61] "inaktiv" "inaktiv" "inaktiv" "inaktiv" ...
#>   ..$ surface_water.conductivity        : tibble [16 × 9] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: chr [1:16] "601" "151" "153" "509" ...
#>   .. ..$ Messstellenname  : chr [1:16] "MPS Berlin-Spandauer-Schifffahrtskanal" "MPS Caprivibrücke" "MPS Charlottenburg" "MPS Landwehrkanal" ...
#>   .. ..$ Gewaesser        : chr [1:16] "Berlin-Spandauer-Schifffahrtskanal" "Spree" "Spree" "Landwehrkanal" ...
#>   .. ..$ Betreiber        : chr [1:16] "Land Berlin" "Land Berlin" "Land Berlin" "Land Berlin" ...
#>   .. ..$ Datum            : chr [1:16] "26.10.2021 10:15" "26.10.2021 10:45" "26.10.2021 11:00" "26.10.2021 12:15" ...
#>   .. ..$ Leitfaehigkeit   : chr [1:16] "856" "842" "827" "831" ...
#>   .. ..$ Einheit          : chr [1:16] "µS/cm" "µS/cm" "µS/cm" "µS/cm" ...
#>   .. ..$ Ganglinie        : logi [1:16] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:16] "inaktiv" "inaktiv" "inaktiv" "inaktiv" ...
#>   ..$ surface_water.ph                  : tibble [16 × 8] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: chr [1:16] "601" "151" "153" "509" ...
#>   .. ..$ Messstellenname  : chr [1:16] "MPS Berlin-Spandauer-Schifffahrtskanal" "MPS Caprivibrücke" "MPS Charlottenburg" "MPS Landwehrkanal" ...
#>   .. ..$ Gewaesser        : chr [1:16] "Berlin-Spandauer-Schifffahrtskanal" "Spree" "Spree" "Landwehrkanal" ...
#>   .. ..$ Betreiber        : chr [1:16] "Land Berlin" "Land Berlin" "Land Berlin" "Land Berlin" ...
#>   .. ..$ Datum            : chr [1:16] "26.10.2021 10:15" "26.10.2021 10:45" "26.10.2021 11:00" "26.10.2021 12:15" ...
#>   .. ..$ pHWert           : chr [1:16] "7.69" "7.44" "7.56" "7.39" ...
#>   .. ..$ Ganglinie        : logi [1:16] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:16] "inaktiv" "inaktiv" "inaktiv" "inaktiv" ...
#>   ..$ surface_water.oxygen_concentration: tibble [16 × 9] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: chr [1:16] "601" "151" "153" "509" ...
#>   .. ..$ Messstellenname  : chr [1:16] "MPS Berlin-Spandauer-Schifffahrtskanal" "MPS Caprivibrücke" "MPS Charlottenburg" "MPS Landwehrkanal" ...
#>   .. ..$ Gewaesser        : chr [1:16] "Berlin-Spandauer-Schifffahrtskanal" "Spree" "Spree" "Landwehrkanal" ...
#>   .. ..$ Betreiber        : chr [1:16] "Land Berlin" "Land Berlin" "Land Berlin" "Land Berlin" ...
#>   .. ..$ Datum            : chr [1:16] "26.10.2021 10:15" "26.10.2021 10:45" "26.10.2021 11:00" "26.10.2021 12:15" ...
#>   .. ..$ Sauerstoffgehalt : chr [1:16] "8.83" "7.55" "8.41" "6.67" ...
#>   .. ..$ Einheit          : chr [1:16] "mg/l" "mg/l" "mg/l" "mg/l" ...
#>   .. ..$ Ganglinie        : logi [1:16] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:16] "inaktiv" "inaktiv" "inaktiv" "inaktiv" ...
#>   ..$ surface_water.oxygen_saturation   : tibble [16 × 9] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: chr [1:16] "601" "151" "153" "509" ...
#>   .. ..$ Messstellenname  : chr [1:16] "MPS Berlin-Spandauer-Schifffahrtskanal" "MPS Caprivibrücke" "MPS Charlottenburg" "MPS Landwehrkanal" ...
#>   .. ..$ Gewaesser        : chr [1:16] "Berlin-Spandauer-Schifffahrtskanal" "Spree" "Spree" "Landwehrkanal" ...
#>   .. ..$ Betreiber        : chr [1:16] "Land Berlin" "Land Berlin" "Land Berlin" "Land Berlin" ...
#>   .. ..$ Datum            : chr [1:16] "26.10.2021 10:15" "26.10.2021 10:45" "26.10.2021 11:00" "26.10.2021 12:15" ...
#>   .. ..$ Parameterwert    : chr [1:16] "79.28" "67.29" "75.12" "59.21" ...
#>   .. ..$ Einheit          : chr [1:16] "%" "%" "%" "%" ...
#>   .. ..$ Ganglinie        : logi [1:16] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:16] "inaktiv" "inaktiv" "inaktiv" "inaktiv" ...
#>   ..$ groundwater.level                 : tibble [868 × 10] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer        : int [1:868] 1 2 3 4 9 21 24 25 26 30 ...
#>   .. ..$ Bezirk                   : chr [1:868] "Reinickendorf" "Reinickendorf" "Reinickendorf" "Reinickendorf" ...
#>   .. ..$ Auspraegung              : chr [1:868] "GW-Stand" "GW-Stand" "GW-Stand + GW-Güte" "GW-Stand" ...
#>   .. ..$ Grundwasserleiter        : chr [1:868] "Hauptgrundwasserleiter (GWL 1.3 + 2)" "Hauptgrundwasserleiter (GWL 1.3 + 2)" "Hauptgrundwasserleiter (GWL 1.3 + 2)" "Hauptgrundwasserleiter (GWL 1.3 + 2)" ...
#>   .. ..$ Grundwasserspannung      : chr [1:868] "gespannt" "ungespannt" "gespannt" "ungespannt" ...
#>   .. ..$ Datum                    : chr [1:868] "09.03.2022" "09.03.2022" "09.03.2022" "09.03.2022" ...
#>   .. ..$ Grundwasserstand_m_ue_NHN: num [1:868] 33.8 35.4 33.9 32.7 37.5 ...
#>   .. ..$ Flur_abstand_m_u_GOK     : chr [1:868] "keine Angabe" "2.30" "keine Angabe" "7.22" ...
#>   .. ..$ Ganglinie                : logi [1:868] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation           : chr [1:868] "extrem niedrig" "normal" "normal" "normal" ...
#>   ..$ groundwater.quality               : tibble [209 × 9] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ Messstellennummer: int [1:209] 3 145 149 282 344 358 499 580 604 645 ...
#>   .. ..$ Bezirk           : chr [1:209] "Reinickendorf" "Reinickendorf" "Mitte" "Mitte" ...
#>   .. ..$ Auspraegung      : chr [1:209] "GW-Stand + GW-Güte" "GW-Stand + GW-Güte" "GW-Stand + GW-Güte" "GW-Stand + GW-Güte" ...
#>   .. ..$ Grundwasserleiter: chr [1:209] "Hauptgrundwasserleiter (GWL 1.3 + 2)" "Hauptgrundwasserleiter (GWL 1.3 + 2)" "Hauptgrundwasserleiter (GWL 1.3 + 2)" "Hauptgrundwasserleiter (GWL 1.3 + 2)" ...
#>   .. ..$ Datum            : chr [1:209] "17.11.2021" "18.10.2021" "19.10.2021" "15.11.2021" ...
#>   .. ..$ Parameterwert    : num [1:209] 12.3 11.8 11.8 12.2 13 12.5 12.7 13 15.9 12 ...
#>   .. ..$ Einheit          : chr [1:209] "grd C" "grd C" "grd C" "grd C" ...
#>   .. ..$ Ganglinie        : logi [1:209] NA NA NA NA NA NA ...
#>   .. ..$ Klassifikation   : chr [1:209] "keine" "keine" "keine" "keine" ...
#>  $ overview_df  :Classes 'data.table' and 'data.frame':  1290 obs. of  26 variables:
#>   ..$ key                      : chr [1:1290] "surface_water.water_level" "surface_water.water_level" "surface_water.water_level" "surface_water.water_level" ...
#>   ..$ Messstellennummer        : chr [1:1290] "5865900" "5827103" "5865300" "5819900" ...
#>   ..$ Messstellenname          : chr [1:1290] "Allee der Kosmonauten" "Allendestraße" "Am Bahndamm" "Am Freibad" ...
#>   ..$ Gewaesser                : chr [1:1290] "M.-H.-Grenzgr." "Müggelspree" "Wuhle" "Tegeler Fließ" ...
#>   ..$ Betreiber                : chr [1:1290] "SenUVK" "SenUVK" "SenUVK" "SenUVK" ...
#>   ..$ Datum                    : chr [1:1290] "15.03.2022 05:45" "15.03.2022 05:45" "15.03.2022 00:30" "15.03.2022 05:45" ...
#>   ..$ Wasserstand              : int [1:1290] 5 50 93 92 4 25 65 76 69 37 ...
#>   ..$ Einheit                  : chr [1:1290] "cm" "cm" "cm" "cm" ...
#>   ..$ Ganglinien               : logi [1:1290] NA NA NA NA NA NA ...
#>   ..$ Klassifikation           : chr [1:1290] "niedrig" "niedrig" "niedrig" "normal" ...
#>   ..$ Durchfluss               : num [1:1290] NA NA NA NA NA NA NA NA NA NA ...
#>   ..$ Ganglinie                : logi [1:1290] NA NA NA NA NA NA ...
#>   ..$ Wassertemperatur         : chr [1:1290] NA NA NA NA ...
#>   ..$ Leitfaehigkeit           : chr [1:1290] NA NA NA NA ...
#>   ..$ pHWert                   : chr [1:1290] NA NA NA NA ...
#>   ..$ Sauerstoffgehalt         : chr [1:1290] NA NA NA NA ...
#>   ..$ Parameterwert            : chr [1:1290] NA NA NA NA ...
#>   ..$ Bezirk                   : chr [1:1290] NA NA NA NA ...
#>   ..$ Auspraegung              : chr [1:1290] NA NA NA NA ...
#>   ..$ Grundwasserleiter        : chr [1:1290] NA NA NA NA ...
#>   ..$ Grundwasserspannung      : chr [1:1290] NA NA NA NA ...
#>   ..$ Grundwasserstand_m_ue_NHN: num [1:1290] NA NA NA NA NA NA NA NA NA NA ...
#>   ..$ Flur_abstand_m_u_GOK     : chr [1:1290] NA NA NA NA ...
#>   ..$ water_body               : chr [1:1290] "surface_water" "surface_water" "surface_water" "surface_water" ...
#>   ..$ variable                 : chr [1:1290] "water_level" "water_level" "water_level" "water_level" ...
#>   ..$ station_type             : chr [1:1290] "ws" "ws" "ws" "ws" ...
#>   ..- attr(*, ".internal.selfref")=<externalptr> 
#>  $ crosstable   : tibble [996 × 11] (S3: tbl_df/tbl/data.frame)
#>   ..$ Messstellennummer: chr [1:996] "5865900" "5827103" "5865300" "5819900" ...
#>   ..$ Messstellenname  : chr [1:996] "Allee der Kosmonauten" "Allendestraße" "Am Bahndamm" "Am Freibad" ...
#>   ..$ ws               : chr [1:996] "x" "x" "x" "x" ...
#>   ..$ df               : chr [1:996] NA "x" "x" NA ...
#>   ..$ wt               : chr [1:996] NA NA "x" NA ...
#>   ..$ lf               : chr [1:996] NA NA NA NA ...
#>   ..$ ph               : chr [1:996] NA NA NA NA ...
#>   ..$ og               : chr [1:996] NA NA NA NA ...
#>   ..$ os               : chr [1:996] NA NA NA NA ...
#>   ..$ gws              : chr [1:996] NA NA NA NA ...
#>   ..$ gwq              : chr [1:996] NA NA NA NA ...

jsonlite::write_json(stations$crosstable, 
                     path = "stations_crosstable.json",
                     pretty = TRUE)
DT::datatable(stations$crosstable, filter = "top", caption = "Data availabilty 
              per monitoring station")

The crosstable data for checking data availabilty of the monitoring stations is also available in JSON format here: https://kwb-r.github.io/wasserportal/stations_crosstable.json

GW level

Master data

Overview data of GW level stations can be requested as shown below:


DT::datatable(stations$overview_list$groundwater.level, filter = "top")

Master data of GW level stations can be requested as shown below:

stations_gwl_master <- wasserportal::get_wasserportal_masters_data(
  station_ids = stations$overview_list$groundwater.level$Messstellennummer
)
#> Importing 868 station metadata from Wasserportal Berlin ... ok. (8.57s)

jsonlite::write_json(stations_gwl_master, 
                     path = "stations_gwl_master.json",
                     pretty = TRUE)

DT::datatable(stations_gwl_master, filter = "top")

The master data of GW level stations is also available in JSON format here: https://kwb-r.github.io/wasserportal/stations_gwl_master.json

Trend Classification

GW level trend classification (provided by SenWeb) is visualized below.

Trend Classification Histogramm

gwl <- stations$overview_list$groundwater.level %>% 
  dplyr::mutate(Datum = as.Date(Datum, format = "%d.%m.%Y"))

text_low_levels <- c("extrem niedrig", "sehr niedrig", "niedrig")
text_high_levels <- c("hoch", "sehr hoch", "extrem hoch")
levels_ordered <- c(text_low_levels, "normal", text_high_levels, "keine")

gwl$Klassifikation <- forcats::fct_relevel(gwl$Klassifikation, levels_ordered)

gwl_classified_only <- gwl %>% dplyr::filter(Klassifikation != "keine")

percental_share_low_levels <- 100*sum(gwl_classified_only$Klassifikation %in% text_low_levels)/nrow(gwl_classified_only) 

percental_share_high_levels <- 100*sum(gwl_classified_only$Klassifikation %in% text_high_levels)/nrow(gwl_classified_only) 

title_text <- sprintf("GW level classification (n = %d out of %d have 'classification' data)", nrow(gwl_classified_only), nrow(gwl))

g1 <- gwl_classified_only %>% 
  dplyr::count(Klassifikation, Grundwasserspannung) %>% 
  dplyr::mutate(percental_share = 100 * n / nrow(gwl)) %>% 
  ggplot2::ggplot(ggplot2::aes_string(x = "Klassifikation",
                                      y = "percental_share",
                                      fill = "Grundwasserspannung")) +
  ggplot2::geom_bar(stat = "identity") + 
  ggplot2::labs(title = title_text,
                x = "Classification",
                y = "Percental share (%)") +
  ggplot2::theme_bw()

plotly::ggplotly(g1)

47.54 percent of all considered 852 GW level monitoring stations containing classification data (out of 868 provided by SenWeb) indicate below normal (extrem niedrig, sehr niedrig, niedrig) GW levels. However, only 47.54 percent are indicate above normal (hoch, sehr hoch, extrem hoch) GW levels.

Trend Classification Map
level_colors <- data.frame(Klassifikation = levels_ordered, classi_color = c(
  "darkred", "red", "orange", "green", "lightblue", "blue", "darkblue", "grey"
))

gwl_classified_only_with_coords <- gwl_classified_only %>% 
  dplyr::mutate(
    Messstellennummer = as.character(Messstellennummer),
  ) %>% 
  dplyr::left_join(
    stations_gwl_master %>%
      dplyr::select("Nummer", "Rechtswert_UTM_33_N", "Hochwert_UTM_33_N") %>% 
      dplyr::rename(Messstellennummer = "Nummer"),
    by = "Messstellennummer"
  ) %>% 
  dplyr::left_join(
    level_colors, 
    by = "Klassifikation"
  ) %>% 
  sf::st_as_sf(
    coords = c("Rechtswert_UTM_33_N", "Hochwert_UTM_33_N"),
    crs = 25833
  ) %>% 
  sf::st_transform(crs = 4326)

# Create a vector of labels for each row in gwl_classified_only_with_coords
labs <- wasserportal::columns_to_labels(
  data = gwl_classified_only_with_coords, 
  columns = c(
    "Messstellennummer", 
    "Grundwasserspannung", 
    "Klassifikation", 
    "Datum"
  ),
  fmt = "<p>%s: %s</p>",
  sep = ""
)

# Print Map
gwlmap <- gwl_classified_only_with_coords %>% 
  leaflet::leaflet() %>%
  leaflet::addTiles() %>% 
  leaflet::addProviderTiles(leaflet::providers$CartoDB.Positron) %>%
  leaflet::addCircles(
    color = ~classi_color,
    label = lapply(labs, htmltools::HTML)
  ) %>% 
  leaflet::addLegend(
    position = "topright",
    colors = level_colors$classi_color,
    labels = level_colors$Klassifikation,
    title = sprintf(
      "Classification (latest data: %s)",
      max(gwl_classified_only_with_coords$Datum)
    )
  )

htmlwidgets::saveWidget(
  gwlmap, 
  "./map_gwl-trend.html", 
  title = "GW level trend"
)

gwlmap

GW level trend plot is also available on a full html page here: https://kwb-r.github.io/wasserportal/map_gwl-trend.html

Download and Plotting One Station

for total period available.

station_gwl <- stations$overview_list$groundwater.level[1,]
ncols <- 2:ncol(station_gwl)

gw_level <- wasserportal::read_wasserportal_raw_gw(
  station = station_gwl$Messstellennummer, 
  stype = "gwl") %>% 
dplyr::mutate(Label = sprintf("%s (%s)", Parameter, Einheit))
head(gw_level)
#> # A tibble: 6 × 6
#>   Messstellennummer Datum      Parameter Einheit  Messwert Label              
#>               <int> <date>     <chr>     <chr>       <dbl> <chr>              
#> 1                 1 1970-01-02 GW-Stand  m ü. NHN     35.2 GW-Stand (m ü. NHN)
#> 2                 1 1970-01-16 GW-Stand  m ü. NHN     35.2 GW-Stand (m ü. NHN)
#> 3                 1 1970-02-02 GW-Stand  m ü. NHN     35.2 GW-Stand (m ü. NHN)
#> 4                 1 1970-02-16 GW-Stand  m ü. NHN     35.2 GW-Stand (m ü. NHN)
#> 5                 1 1970-03-02 GW-Stand  m ü. NHN     35.2 GW-Stand (m ü. NHN)
#> 6                 1 1970-03-16 GW-Stand  m ü. NHN     35.2 GW-Stand (m ü. NHN)

g <- gw_level %>% 
ggplot2::ggplot(ggplot2::aes_string(x = "Datum", y = "Messwert", col = "Label")) +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::theme_bw()


title_subtitle <- paste0(paste0(names(station_gwl)[1], ": ", 
                             station_gwl[1], 
                             collapse =", "),
       "<br>",
       "<sup>",
       paste0(names(station_gwl)[ncols], ": ",
              station_gwl[ncols], 
              collapse =", "),
       "</sup>")


plotly::ggplotly(g) %>%
  plotly::layout(title = list(text = title_subtitle))

Download and Plotting Multiple Stations

gw_level_multi <- data.table::rbindlist(
  lapply(stations$overview_list$groundwater.level$Messstellennummer[1:5], 
                     function(id) {
                       wasserportal::read_wasserportal_raw_gw(
                         station = id, stype = "gwl")
                     }))


jsonlite::write_json(gw_level_multi,
                     path = "stations_gwl_data.json",
                     pretty = TRUE)


# Plot 10 GW level
selected_stations <- stations$overview_list$groundwater.level$Messstellennummer[1:10]

g <- gw_level_multi %>% 
dplyr::filter(Messstellennummer %in% selected_stations) %>% 
dplyr::mutate(Messstellennummer = as.character(Messstellennummer)) %>% 
ggplot2::ggplot(ggplot2::aes_string(x = "Datum", 
                                    y = "Messwert", 
                                    col = "Messstellennummer")) +
ggplot2::labs(title = "GW level (m above NN)") +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::theme_bw()

plotly::ggplotly(g)

The data of all GW level stations is also available in JSON format here: https://kwb-r.github.io/wasserportal/stations_gwl_data.json

GW quality

Overview data of GW level stations can be requested as shown below:

stations_gwq <- wasserportal::get_wasserportal_stations_table(
  type = overview_options$groundwater$quality
  )

DT::datatable(stations_gwq, filter = "top")

Master data of GW quality stations can be requested as shown below:

stations_gwq_master <- wasserportal::get_wasserportal_masters_data(
  station_ids = stations_gwq$Messstellennummer
)
#> Importing 209 station metadata from Wasserportal Berlin ... ok. (2.08s)

jsonlite::write_json(stations_gwq_master, 
                     path = "stations_gwq_master.json",
                     pretty = TRUE)

The master data of GW quality stations is also available in JSON format here: https://kwb-r.github.io/wasserportal/stations_gwq_master.json

GW Quality: Download and Plotting One Station

station_gwq <- stations$overview_list$groundwater.quality[1,]
ncols <- 2:ncol(station_gwq)

gw_quality <- wasserportal::read_wasserportal_raw_gw(
  station = station_gwq$Messstellennummer, 
  stype = "gwq")
head(gw_quality)
#> # A tibble: 6 × 5
#>   Messstellennummer Datum      Parameter                  Einheit      Messwert
#>               <int> <date>     <chr>                      <chr>           <dbl>
#> 1                 3 2020-07-01 Temperatur (Luft)          grd Celsius     19   
#> 2                 3 2020-07-01 pH-Wert (Feld)             ohne Einheit     7.1 
#> 3                 3 2020-07-01 Temperatur (Wasser)        grd C           12.2 
#> 4                 3 2020-07-01 Leitfähigkeit 25°C vor Ort µS/cm          939   
#> 5                 3 2020-07-01 Wasserst.(ROK) vor         m                4.91
#> 6                 3 2020-07-01 Wasserst.(ROK) nach        m                5

unique(gw_quality$Parameter)
#>  [1] "Temperatur (Luft)"            "pH-Wert (Feld)"              
#>  [3] "Temperatur (Wasser)"          "Leitfähigkeit 25°C vor Ort"  
#>  [5] "Wasserst.(ROK) vor"           "Wasserst.(ROK) nach"         
#>  [7] "Entnahmeteufe (ROK)"          "Förderrate"                  
#>  [9] "Redox Pumpbeginn"             "O2-Gehalt Pumpbeg."          
#> [11] "Redox Pumpende"               "pH Pumpende"                 
#> [13] "O2-Gehalt Pumpende"           "Chlorid"                     
#> [15] "Fluorid"                      "Hydrogenkarbonat"            
#> [17] "Nitrit (N)"                   "Nitrat (N)"                  
#> [19] "Orhto-Phosphat (P)"           "Sulfat"                      
#> [21] "Cyanide (ges.)"               "Bromid"                      
#> [23] "Nitrit"                       "Nitrat"                      
#> [25] "Ortho-Phosphat"               "Ammonium (N)"                
#> [27] "Eisen-2"                      "Eisen (ges.)"                
#> [29] "Kalium"                       "Kalzium"                     
#> [31] "Magnesium"                    "Natrium"                     
#> [33] "Mangan"                       "Ammonium"                    
#> [35] "Leitfähigkeit /Lab. bei 25°C" "UV-Adsorption (254)"         
#> [37] "CSV (KMNO4)"                  "Basenkap. bis 8.2"           
#> [39] "Säure-Kap. bis 4.3"           "Kohlenstoff (organ.)"        
#> [41] "pH-Wert /Lab."                "Gesamthärte"                 
#> [43] "Karbonathärte"                "AOX"                         
#> [45] "Phenolindex (ges.)"           "Arsen"                       
#> [47] "Barium"                       "Blei"                        
#> [49] "Bor"                          "Cadmium"                     
#> [51] "Chrom"                        "Kupfer"                      
#> [53] "Aluminium-gelöst"             "Molybdän"                    
#> [55] "Nickel"                       "Quecksilber"                 
#> [57] "Selen"                        "Zink"                        
#> [59] "Vanadium"                     "Thallium"                    
#> [61] "Uran"                         "Summe Na+Cl"                 
#> [63] "Ionenbilanz (Labor)"

g <- gw_quality %>%  
dplyr::filter(Parameter == "Sulfat") %>% 
ggplot2::ggplot(ggplot2::aes_string(x = "Datum", y = "Messwert", col = "Parameter")) +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::theme_bw()


title_subtitle <- paste0(paste0(names(station_gwq)[1], ": ", 
                             station_gwq[1], 
                             collapse =", "),
       "<br>",
       "<sup>",
       paste0(names(station_gwq)[ncols], ": ",
              station_gwq[ncols], 
              collapse =", "),
       "</sup>")


plotly::ggplotly(g) %>%
  plotly::layout(title = list(text = title_subtitle))

GW Quality: Download and Plotting Multiple Stations

gw_quality_multi <- data.table::rbindlist(
  lapply(stations$overview_list$groundwater.quality$Messstellennummer, 
                     function(id) {
                       wasserportal::read_wasserportal_raw_gw(
                         station = id, stype = "gwq")
                     }))


jsonlite::write_json(gw_quality_multi,
                     path = "stations_gwq_data.json",
                     pretty = TRUE)


# Plot 10 GW quality 
selected_stations <- stations$overview_list$groundwater.quality$Messstellennummer[1:10]

g <- gw_quality_multi %>% 
dplyr::filter(Messstellennummer %in% selected_stations) %>% 
dplyr::mutate(Messstellennummer = as.character(Messstellennummer)) %>% 
dplyr::filter(Parameter == "Sulfat") %>% 
ggplot2::ggplot(ggplot2::aes_string(x = "Datum", 
                                    y = "Messwert", 
                                    col = "Messstellennummer")) +
ggplot2::labs(title = "GW quality (Sulfat)") +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::theme_bw()

plotly::ggplotly(g)

The data of all GW quality stations is also available in JSON format here: https://kwb-r.github.io/wasserportal/stations_gwq_data.json